The history of heart rate analysis can be traced back to the turn of the century when correlations between heart rate variations and the underlying physiological and psychological state of a human subject began to be investigated with several early pioneers such as Willem Einthoven.
The arrival of computerized methodologies in the 1950s and 60s greatly increased the ability to identify underlying patterns and led to an increased understanding of the heart's control systems.
The variations and patterns of the heart rate over time can be used to infer actions caused by the two key control subsystems of the autonomic nervous system (ANS): the sympathetic subsystem and the parasympathetic subsystem. Respiratory Sinus Arrhythmia (RSA) is one such variation of heart rate that occurs through the influence of breathing on the balance of sympathetic and parasympathetic control over the heart muscle. This is due to a reducing of effect of the parasympathetic during inhalation via baroreflex action (reduced blood pressure) which causes the heart rate to increase and a reversal of this effect during exhalation where heart rate is seen to decrease. FIG. 1 illustrates the type of heart rate pattern seen when the RSA component is strong. This effect is most noticeable in rest conditions where the balance is shifted towards parasympathetic control and so these baroreflex effects become key modulators within the system. During periods where the sympathetic system dominates, the RSA component in the heart rate data can be negligible, as illustrated in FIG. 2.
The respiratory sinus arrhythmia (RSA) pattern was first noticed within cardiograms in the 1950s and was being used by clinicians as a test of autonomic balance by the 1970's. Wheeler and Watkins, in 1973, document a method of measuring a subject's RSA by comparing the highest and lowest heart rate achieved during a 5 minute period while breathing deeply at a rate of 6 breaths per minute.
In the 1980s a measure of RSA was first utilized within a biofeedback framework by Alexander Smetankin with the invention of his cardiosignalizer, a device for providing audio and visual feedback of the RSA component of the heart rate signal using a methodology similar to that described by Wheeler and Watkins.
As RSA strength is a key sign of autonomic balance it can be used within biofeedback applications to help achieve deeper levels of relaxation. In practice this means linking deep breathing exercises, which induce stronger RSA heart patterns, with visual or audio feedback of RSA strength to train users in breathing based relaxation techniques. The state reached via these exercises is sometime called “cardiac coherence” and is indicated by a high RSA component of the heart signal.
Biofeedback is the process of presenting back to a person a metric of some aspect of their physical state that they are not usually conscious of so that they can be trained to develop stronger conscious control over this physical property. A classic example of this would be a person with balance control problems standing on a balance board which measures their center of gravity and changing the volume or frequency of an audio tone depending on how far their center of gravity moves from some target position. In this way a person can learn to adapt their muscle control based on this feedback and so achieve better balance. When administered therapeutically, biofeedback is typically administered in a series of sessions by a trained technician using a biofeedback device to a subject.
RSA based biofeedback has been shown to be useful in controlling anxiety and stress and clinical trials have shown its efficacy in reducing high blood pressure. Instantaneous RSA can be used as a biofeedback parameter to modulate audiovisual representations to a subject or user, giving him/her an indication of how a paced breathing exercise is affecting his/her heart patterns. Simple examples of this include modulating the screen color displayed to the user or modulating the frequency of an audio tone played to the subject.
A general measure of RSA strength, S′(t), can be defined as a maximum of the cross correlation function of the heart rate signal H(t) and a function of breathing rate B(t) calculated over a synchronous window of time as in Equation 1 below:S′(t)=max(H(t)*B(t))  (1)where * represents the cross-correlation function applied to the two signals over the same time interval and max represents the maximum value function.
As breathing rate can be less convenient to measure than heart rate, the measure S′(t) is often estimated with B(t), defined a priori as a sinusoidal function with amplitude 1.0 and a target breathing frequency at which the user is instructed to breath at, usually defined in the range 0.05 to 0.2 Hertz. This allows S′(t) to be estimated without direct measure of breath air flow or lung volume.
Several sensor technologies can be utilized to transduce heart activity into electrical signals available for processing and RSA feature analysis. These sensors include microphone (audio heart signals), pressure sensors (pulse pressure), electrocardiogram (ECG), photoplethysmography (PPG), as well as non-contact sensors that utilize RF technologies.
In recent years, consumer PPG products have appeared on the market. A coincident trend is the adoption of mobile devices with graphical user interfaces, audiovisual and wireless capabilities. As mobile devices become increasingly ubiquitous, a wireless PPG or ECG sensor product that communicates sensor data to a mobile device that performs post-processing is desirable since it can reduce cost by working in a subject's existing mobile device and it can be very convenient to use.
In such a system where an external computing device (such as mobile phone) performs the digital signal processing required for RSA feature extraction there is a need for a robust and highly scalable method that can achieve this goal within the context of widely varying computational abilities of mobile devices on the market.
However, current methods for determining RSA based on cross correlation or power spectrum analysis are often too computationally demanding to run in real time on many mobile devices. Thus there is a need in the market for a method of extracting the RSA component of a heart rate signal that can be used across a wide spectrum of computing devices, including mobile devices that may be resource constrained.